Overview

Dataset statistics

Number of variables30
Number of observations160359
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory36.7 MiB
Average record size in memory240.0 B

Variable types

Numeric27
Categorical3

Alerts

Unnamed: 0.1 is highly correlated with unit_IDHigh correlation
unit_ID is highly correlated with Unnamed: 0.1High correlation
cycles is highly correlated with RULHigh correlation
setting_1 is highly correlated with setting_2 and 19 other fieldsHigh correlation
setting_2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
setting_3 is highly correlated with T24 and 12 other fieldsHigh correlation
T2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
T24 is highly correlated with setting_1 and 21 other fieldsHigh correlation
T30 is highly correlated with setting_1 and 21 other fieldsHigh correlation
T50 is highly correlated with setting_1 and 21 other fieldsHigh correlation
P2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
P15 is highly correlated with setting_1 and 19 other fieldsHigh correlation
P30 is highly correlated with setting_1 and 19 other fieldsHigh correlation
Nf is highly correlated with setting_1 and 21 other fieldsHigh correlation
Nc is highly correlated with setting_1 and 21 other fieldsHigh correlation
epr is highly correlated with setting_1 and 21 other fieldsHigh correlation
Ps30 is highly correlated with setting_1 and 21 other fieldsHigh correlation
phi is highly correlated with setting_1 and 19 other fieldsHigh correlation
NRf is highly correlated with setting_3 and 1 other fieldsHigh correlation
NRc is highly correlated with setting_1 and 21 other fieldsHigh correlation
BPR is highly correlated with setting_1 and 21 other fieldsHigh correlation
farB is highly correlated with setting_1 and 19 other fieldsHigh correlation
htBleed is highly correlated with setting_1 and 21 other fieldsHigh correlation
Nf_dmd is highly correlated with setting_1 and 21 other fieldsHigh correlation
PCNfR_dmd is highly correlated with setting_3 and 12 other fieldsHigh correlation
W31 is highly correlated with setting_1 and 19 other fieldsHigh correlation
W32 is highly correlated with setting_1 and 19 other fieldsHigh correlation
RUL is highly correlated with cyclesHigh correlation
Unnamed: 0.1 is highly correlated with unit_IDHigh correlation
unit_ID is highly correlated with Unnamed: 0.1High correlation
cycles is highly correlated with RULHigh correlation
setting_1 is highly correlated with setting_2 and 18 other fieldsHigh correlation
setting_2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
setting_3 is highly correlated with T30 and 11 other fieldsHigh correlation
T2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
T24 is highly correlated with setting_1 and 19 other fieldsHigh correlation
T30 is highly correlated with setting_1 and 22 other fieldsHigh correlation
T50 is highly correlated with setting_1 and 22 other fieldsHigh correlation
P2 is highly correlated with setting_1 and 19 other fieldsHigh correlation
P15 is highly correlated with setting_1 and 19 other fieldsHigh correlation
P30 is highly correlated with setting_1 and 19 other fieldsHigh correlation
Nf is highly correlated with setting_1 and 22 other fieldsHigh correlation
Nc is highly correlated with setting_1 and 22 other fieldsHigh correlation
epr is highly correlated with setting_1 and 22 other fieldsHigh correlation
Ps30 is highly correlated with setting_1 and 22 other fieldsHigh correlation
phi is highly correlated with setting_1 and 19 other fieldsHigh correlation
NRf is highly correlated with setting_3 and 11 other fieldsHigh correlation
NRc is highly correlated with setting_2 and 21 other fieldsHigh correlation
BPR is highly correlated with setting_1 and 22 other fieldsHigh correlation
farB is highly correlated with setting_1 and 19 other fieldsHigh correlation
htBleed is highly correlated with setting_1 and 22 other fieldsHigh correlation
Nf_dmd is highly correlated with setting_1 and 22 other fieldsHigh correlation
PCNfR_dmd is highly correlated with setting_3 and 11 other fieldsHigh correlation
W31 is highly correlated with setting_1 and 19 other fieldsHigh correlation
W32 is highly correlated with setting_1 and 19 other fieldsHigh correlation
RUL is highly correlated with cyclesHigh correlation
Unnamed: 0.1 is highly correlated with unit_IDHigh correlation
unit_ID is highly correlated with Unnamed: 0.1High correlation
setting_1 is highly correlated with setting_2 and 18 other fieldsHigh correlation
setting_2 is highly correlated with setting_1 and 18 other fieldsHigh correlation
setting_3 is highly correlated with PCNfR_dmdHigh correlation
T2 is highly correlated with setting_1 and 18 other fieldsHigh correlation
T24 is highly correlated with setting_1 and 19 other fieldsHigh correlation
T30 is highly correlated with setting_1 and 19 other fieldsHigh correlation
T50 is highly correlated with setting_1 and 19 other fieldsHigh correlation
P2 is highly correlated with setting_1 and 18 other fieldsHigh correlation
P15 is highly correlated with setting_1 and 18 other fieldsHigh correlation
P30 is highly correlated with setting_1 and 19 other fieldsHigh correlation
Nf is highly correlated with setting_1 and 19 other fieldsHigh correlation
Nc is highly correlated with setting_1 and 19 other fieldsHigh correlation
epr is highly correlated with setting_1 and 19 other fieldsHigh correlation
Ps30 is highly correlated with setting_1 and 19 other fieldsHigh correlation
phi is highly correlated with setting_1 and 19 other fieldsHigh correlation
NRc is highly correlated with T24 and 12 other fieldsHigh correlation
BPR is highly correlated with setting_1 and 19 other fieldsHigh correlation
farB is highly correlated with setting_1 and 19 other fieldsHigh correlation
htBleed is highly correlated with setting_1 and 19 other fieldsHigh correlation
Nf_dmd is highly correlated with setting_1 and 19 other fieldsHigh correlation
PCNfR_dmd is highly correlated with setting_3High correlation
W31 is highly correlated with setting_1 and 18 other fieldsHigh correlation
W32 is highly correlated with setting_1 and 18 other fieldsHigh correlation
PCNfR_dmd is highly correlated with setting_3High correlation
setting_3 is highly correlated with PCNfR_dmdHigh correlation
Unnamed: 0 is highly correlated with Unnamed: 0.1 and 19 other fieldsHigh correlation
Unnamed: 0.1 is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
unit_ID is highly correlated with Unnamed: 0 and 1 other fieldsHigh correlation
cycles is highly correlated with failure and 1 other fieldsHigh correlation
setting_1 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
setting_2 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
setting_3 is highly correlated with setting_1 and 21 other fieldsHigh correlation
T2 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
T24 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
T30 is highly correlated with setting_1 and 22 other fieldsHigh correlation
T50 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
P2 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
P15 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
P30 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
Nf is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
Nc is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
epr is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
Ps30 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
phi is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
NRf is highly correlated with setting_1 and 21 other fieldsHigh correlation
NRc is highly correlated with setting_1 and 22 other fieldsHigh correlation
BPR is highly correlated with setting_1 and 22 other fieldsHigh correlation
farB is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
htBleed is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
Nf_dmd is highly correlated with Unnamed: 0 and 20 other fieldsHigh correlation
PCNfR_dmd is highly correlated with setting_1 and 21 other fieldsHigh correlation
W31 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
W32 is highly correlated with Unnamed: 0 and 23 other fieldsHigh correlation
failure is highly correlated with cycles and 1 other fieldsHigh correlation
RUL is highly correlated with cycles and 1 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
setting_2 has 13484 (8.4%) zeros Zeros

Reproduction

Analysis started2022-04-13 14:45:24.012938
Analysis finished2022-04-13 14:48:15.448106
Duration2 minutes and 51.44 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct160359
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80179
Minimum0
Maximum160358
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:15.637724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8017.9
Q140089.5
median80179
Q3120268.5
95-th percentile152340.1
Maximum160358
Range160358
Interquartile range (IQR)80179

Descriptive statistics

Standard deviation46291.80025
Coefficient of variation (CV)0.5773556698
Kurtosis-1.2
Mean80179
Median Absolute Deviation (MAD)40090
Skewness0
Sum1.285742426 × 1010
Variance2142930770
MonotonicityStrictly increasing
2022-04-13T20:18:15.817229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1069091
 
< 0.1%
1069021
 
< 0.1%
1069031
 
< 0.1%
1069041
 
< 0.1%
1069051
 
< 0.1%
1069061
 
< 0.1%
1069071
 
< 0.1%
1069081
 
< 0.1%
1069101
 
< 0.1%
Other values (160349)160349
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
1603581
< 0.1%
1603571
< 0.1%
1603561
< 0.1%
1603551
< 0.1%
1603541
< 0.1%
1603531
< 0.1%
1603521
< 0.1%
1603511
< 0.1%
1603501
< 0.1%
1603491
< 0.1%

Unnamed: 0.1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct61249
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23940.1165
Minimum0
Maximum61248
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:16.141716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2004
Q110022
median20044
Q337458.5
95-th percentile53494.1
Maximum61248
Range61248
Interquartile range (IQR)27436.5

Descriptive statistics

Standard deviation16645.56457
Coefficient of variation (CV)0.6953000654
Kurtosis-0.9316470364
Mean23940.1165
Median Absolute Deviation (MAD)12486
Skewness0.4780105119
Sum3839013142
Variance277074819.9
MonotonicityNot monotonic
2022-04-13T20:18:16.311041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04
 
< 0.1%
137814
 
< 0.1%
137594
 
< 0.1%
137584
 
< 0.1%
137574
 
< 0.1%
137564
 
< 0.1%
137554
 
< 0.1%
137544
 
< 0.1%
137534
 
< 0.1%
137524
 
< 0.1%
Other values (61239)160319
> 99.9%
ValueCountFrequency (%)
04
< 0.1%
14
< 0.1%
24
< 0.1%
34
< 0.1%
44
< 0.1%
54
< 0.1%
64
< 0.1%
74
< 0.1%
84
< 0.1%
94
< 0.1%
ValueCountFrequency (%)
612481
< 0.1%
612471
< 0.1%
612461
< 0.1%
612451
< 0.1%
612441
< 0.1%
612431
< 0.1%
612421
< 0.1%
612411
< 0.1%
612401
< 0.1%
612391
< 0.1%

unit_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct260
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.5537575
Minimum1
Maximum260
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:16.493456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q144
median89
Q3164
95-th percentile237
Maximum260
Range259
Interquartile range (IQR)120

Descriptive statistics

Standard deviation72.86732493
Coefficient of variation (CV)0.6903337849
Kurtosis-0.988565947
Mean105.5537575
Median Absolute Deviation (MAD)56
Skewness0.4458773893
Sum16926495
Variance5309.647042
MonotonicityNot monotonic
2022-04-13T20:18:16.674275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
961305
 
0.8%
881288
 
0.8%
101241
 
0.8%
551221
 
0.8%
941205
 
0.8%
181194
 
0.7%
91140
 
0.7%
341135
 
0.7%
711123
 
0.7%
21108
 
0.7%
Other values (250)148399
92.5%
ValueCountFrequency (%)
1921
0.6%
21108
0.7%
3914
0.6%
4970
0.6%
5829
0.5%
6972
0.6%
71078
0.7%
8798
0.5%
91140
0.7%
101241
0.8%
ValueCountFrequency (%)
260316
0.2%
259205
0.1%
258143
0.1%
257309
0.2%
256163
0.1%
255340
0.2%
254260
0.2%
253149
0.1%
252135
 
0.1%
251266
0.2%

cycles
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct543
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.3313378
Minimum1
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:16.851985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q157
median114
Q3173
95-th percentile276
Maximum543
Range542
Interquartile range (IQR)116

Descriptive statistics

Standard deviation83.53814553
Coefficient of variation (CV)0.6773472745
Kurtosis0.8325328473
Mean123.3313378
Median Absolute Deviation (MAD)58
Skewness0.8471574213
Sum19777290
Variance6978.621759
MonotonicityNot monotonic
2022-04-13T20:18:17.037344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1709
 
0.4%
66709
 
0.4%
96709
 
0.4%
95709
 
0.4%
94709
 
0.4%
93709
 
0.4%
92709
 
0.4%
91709
 
0.4%
90709
 
0.4%
89709
 
0.4%
Other values (533)153269
95.6%
ValueCountFrequency (%)
1709
0.4%
2709
0.4%
3709
0.4%
4709
0.4%
5709
0.4%
6709
0.4%
7709
0.4%
8709
0.4%
9709
0.4%
10709
0.4%
ValueCountFrequency (%)
5431
< 0.1%
5421
< 0.1%
5411
< 0.1%
5401
< 0.1%
5391
< 0.1%
5381
< 0.1%
5371
< 0.1%
5361
< 0.1%
5351
< 0.1%
5341
< 0.1%

setting_1
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct670
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.21197311
Minimum-0.0087
Maximum42.008
Zeros1133
Zeros (%)0.7%
Negative22272
Negative (%)13.9%
Memory size1.2 MiB
2022-04-13T20:18:17.222745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0087
5-th percentile-0.002
Q10.0013
median19.9981
Q335.0015
95-th percentile42.0052
Maximum42.008
Range42.0167
Interquartile range (IQR)35.0002

Descriptive statistics

Standard deviation16.52798837
Coefficient of variation (CV)0.9602611081
Kurtosis-1.476667288
Mean17.21197311
Median Absolute Deviation (MAD)19.9959
Skewness0.3026356858
Sum2760094.796
Variance273.1743996
MonotonicityNot monotonic
2022-04-13T20:18:17.392326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00041465
 
0.9%
0.00021441
 
0.9%
0.00011414
 
0.9%
0.00051410
 
0.9%
0.00061409
 
0.9%
0.00071383
 
0.9%
0.00031373
 
0.9%
0.00091339
 
0.8%
0.00081312
 
0.8%
0.0011290
 
0.8%
Other values (660)146523
91.4%
ValueCountFrequency (%)
-0.00871
 
< 0.1%
-0.00862
< 0.1%
-0.00851
 
< 0.1%
-0.00841
 
< 0.1%
-0.00822
< 0.1%
-0.00812
< 0.1%
-0.00791
 
< 0.1%
-0.00783
< 0.1%
-0.00753
< 0.1%
-0.00744
< 0.1%
ValueCountFrequency (%)
42.008139
0.1%
42.0079306
0.2%
42.0078295
0.2%
42.0077299
0.2%
42.0076287
0.2%
42.0075284
0.2%
42.0074281
0.2%
42.0073258
0.2%
42.0072269
0.2%
42.0071302
0.2%

setting_2
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct111
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4100036605
Minimum-0.0006
Maximum0.842
Zeros13484
Zeros (%)8.4%
Negative20249
Negative (%)12.6%
Memory size1.2 MiB
2022-04-13T20:18:17.572533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.0006
5-th percentile-0.0003
Q10.0002
median0.62
Q30.84
95-th percentile0.8413
Maximum0.842
Range0.8426
Interquartile range (IQR)0.8398

Descriptive statistics

Standard deviation0.367938243
Coefficient of variation (CV)0.8974023366
Kurtosis-1.803435447
Mean0.4100036605
Median Absolute Deviation (MAD)0.222
Skewness-0.01587502946
Sum65747.777
Variance0.1353785506
MonotonicityNot monotonic
2022-04-13T20:18:17.743705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8423617
 
14.7%
013484
 
8.4%
0.258824
 
5.5%
0.628811
 
5.5%
0.78731
 
5.4%
0.00034963
 
3.1%
0.00014956
 
3.1%
0.00024947
 
3.1%
0.00044891
 
3.1%
-0.00034610
 
2.9%
Other values (101)72525
45.2%
ValueCountFrequency (%)
-0.000662
 
< 0.1%
-0.00052105
 
1.3%
-0.00044465
 
2.8%
-0.00034610
 
2.9%
-0.00024580
 
2.9%
-0.00014427
 
2.8%
013484
8.4%
0.00014956
 
3.1%
0.00024947
 
3.1%
0.00034963
 
3.1%
ValueCountFrequency (%)
0.842589
0.4%
0.84191086
0.7%
0.84181146
0.7%
0.84171079
0.7%
0.84161175
0.7%
0.84151148
0.7%
0.84141175
0.7%
0.84131200
0.7%
0.84121104
0.7%
0.84111124
0.7%

setting_3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
100.0
143218 
60.0
17141 

Length

Max length5
Median length5
Mean length4.893108588
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row60.0

Common Values

ValueCountFrequency (%)
100.0143218
89.3%
60.017141
 
10.7%

Length

2022-04-13T20:18:17.901183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T20:18:17.993305image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
100.0143218
89.3%
60.017141
 
10.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

T2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.84089
Minimum445
Maximum518.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:18.075205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum445
5-th percentile445
Q1449.44
median489.05
Q3518.67
95-th percentile518.67
Maximum518.67
Range73.67
Interquartile range (IQR)69.23

Descriptive statistics

Standard deviation30.42038815
Coefficient of variation (CV)0.06261389021
Kurtosis-1.628882978
Mean485.84089
Median Absolute Deviation (MAD)29.62
Skewness-0.1755897193
Sum77908959.28
Variance925.4000151
MonotonicityNot monotonic
2022-04-13T20:18:18.186106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
518.6762633
39.1%
44528853
18.0%
489.0517320
 
10.8%
491.1917213
 
10.7%
449.4417199
 
10.7%
462.5417141
 
10.7%
ValueCountFrequency (%)
44528853
18.0%
449.4417199
 
10.7%
462.5417141
 
10.7%
489.0517320
 
10.8%
491.1917213
 
10.7%
518.6762633
39.1%
ValueCountFrequency (%)
518.6762633
39.1%
491.1917213
 
10.7%
489.0517320
 
10.8%
462.5417141
 
10.7%
449.4417199
 
10.7%
44528853
18.0%

T24
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1799
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.3610218
Minimum535.48
Maximum645.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:18.346497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum535.48
5-th percentile536.76
Q1549.96
median605.93
Q3642.34
95-th percentile643.18
Maximum645.11
Range109.63
Interquartile range (IQR)92.38

Descriptive statistics

Standard deviation42.47851647
Coefficient of variation (CV)0.07111029163
Kurtosis-1.668798013
Mean597.3610218
Median Absolute Deviation (MAD)36.94
Skewness-0.1943949434
Sum95792216.09
Variance1804.424361
MonotonicityNot monotonic
2022-04-13T20:18:18.516996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
642.53508
 
0.3%
642.39507
 
0.3%
642.5505
 
0.3%
642.37504
 
0.3%
642.49493
 
0.3%
642.44493
 
0.3%
642.45493
 
0.3%
642.3491
 
0.3%
642.56490
 
0.3%
642.27487
 
0.3%
Other values (1789)155388
96.9%
ValueCountFrequency (%)
535.482
< 0.1%
535.511
< 0.1%
535.531
< 0.1%
535.561
< 0.1%
535.581
< 0.1%
535.591
< 0.1%
535.612
< 0.1%
535.621
< 0.1%
535.632
< 0.1%
535.641
< 0.1%
ValueCountFrequency (%)
645.111
 
< 0.1%
644.711
 
< 0.1%
644.532
< 0.1%
644.521
 
< 0.1%
644.51
 
< 0.1%
644.472
< 0.1%
644.453
< 0.1%
644.441
 
< 0.1%
644.421
 
< 0.1%
644.413
< 0.1%

T30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15377
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1467.035653
Minimum1242.67
Maximum1616.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:18.693570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1242.67
5-th percentile1261.009
Q11357.36
median1492.81
Q31586.59
95-th percentile1596.99
Maximum1616.91
Range374.24
Interquartile range (IQR)229.23

Descriptive statistics

Standard deviation118.1752606
Coefficient of variation (CV)0.08055377545
Kurtosis-1.348230086
Mean1467.035653
Median Absolute Deviation (MAD)100.22
Skewness-0.3533425157
Sum235252370.2
Variance13965.39221
MonotonicityNot monotonic
2022-04-13T20:18:18.995587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1589.0661
 
< 0.1%
1590.157
 
< 0.1%
1585.9355
 
< 0.1%
1589.5554
 
< 0.1%
1587.8653
 
< 0.1%
1586.4953
 
< 0.1%
1588.2852
 
< 0.1%
1589.5752
 
< 0.1%
1587.1551
 
< 0.1%
1586.2351
 
< 0.1%
Other values (15367)159820
99.7%
ValueCountFrequency (%)
1242.671
< 0.1%
1242.981
< 0.1%
1243.281
< 0.1%
1243.511
< 0.1%
1243.621
< 0.1%
1243.661
< 0.1%
1243.731
< 0.1%
12441
< 0.1%
1244.31
< 0.1%
1244.481
< 0.1%
ValueCountFrequency (%)
1616.911
< 0.1%
1615.392
< 0.1%
1615.011
< 0.1%
1614.931
< 0.1%
1614.721
< 0.1%
1613.831
< 0.1%
1613.621
< 0.1%
1613.291
< 0.1%
16132
< 0.1%
1612.882
< 0.1%

T50
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20582
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1260.956434
Minimum1023.77
Maximum1441.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:19.179630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1023.77
5-th percentile1047.35
Q11126.83
median1271.74
Q31402.2
95-th percentile1418.41
Maximum1441.49
Range417.72
Interquartile range (IQR)275.37

Descriptive statistics

Standard deviation136.3000728
Coefficient of variation (CV)0.1080926105
Kurtosis-1.567224122
Mean1260.956434
Median Absolute Deviation (MAD)136.35
Skewness-0.1917854736
Sum202205712.8
Variance18577.70984
MonotonicityNot monotonic
2022-04-13T20:18:19.349527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1401.9941
 
< 0.1%
1402.8939
 
< 0.1%
1408.6139
 
< 0.1%
1403.7239
 
< 0.1%
1398.6739
 
< 0.1%
1406.5639
 
< 0.1%
1398.1238
 
< 0.1%
1402.9638
 
< 0.1%
1403.7538
 
< 0.1%
1403.3338
 
< 0.1%
Other values (20572)159971
99.8%
ValueCountFrequency (%)
1023.771
< 0.1%
1024.421
< 0.1%
1025.521
< 0.1%
1025.751
< 0.1%
1025.961
< 0.1%
1026.471
< 0.1%
1026.531
< 0.1%
1026.591
< 0.1%
1026.911
< 0.1%
1027.391
< 0.1%
ValueCountFrequency (%)
1441.491
< 0.1%
1441.161
< 0.1%
1440.771
< 0.1%
1439.981
< 0.1%
1439.481
< 0.1%
1439.231
< 0.1%
1439.121
< 0.1%
1439.091
< 0.1%
1438.961
< 0.1%
1438.511
< 0.1%

P2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.894999408
Minimum3.91
Maximum14.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:19.497968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.91
5-th percentile3.91
Q15.48
median9.35
Q314.62
95-th percentile14.62
Maximum14.62
Range10.71
Interquartile range (IQR)9.14

Descriptive statistics

Standard deviation4.265554481
Coefficient of variation (CV)0.4310818329
Kurtosis-1.57026665
Mean9.894999408
Median Absolute Deviation (MAD)5.27
Skewness-0.1166639033
Sum1586752.21
Variance18.19495503
MonotonicityNot monotonic
2022-04-13T20:18:19.613514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
14.6262633
39.1%
3.9128853
18.0%
10.5217320
 
10.8%
9.3517213
 
10.7%
5.4817199
 
10.7%
7.0517141
 
10.7%
ValueCountFrequency (%)
3.9128853
18.0%
5.4817199
 
10.7%
7.0517141
 
10.7%
9.3517213
 
10.7%
10.5217320
 
10.8%
14.6262633
39.1%
ValueCountFrequency (%)
14.6262633
39.1%
10.5217320
 
10.8%
9.3517213
 
10.7%
7.0517141
 
10.7%
5.4817199
 
10.7%
3.9128853
18.0%

P15
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.4249353
Minimum5.67
Maximum21.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:19.763566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5.67
5-th percentile5.71
Q18
median13.66
Q321.61
95-th percentile21.61
Maximum21.61
Range15.94
Interquartile range (IQR)13.61

Descriptive statistics

Standard deviation6.443922376
Coefficient of variation (CV)0.4467210592
Kurtosis-1.638001397
Mean14.4249353
Median Absolute Deviation (MAD)7.93
Skewness-0.0742543589
Sum2313168.2
Variance41.52413559
MonotonicityNot monotonic
2022-04-13T20:18:19.939448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.6145423
28.3%
812991
 
8.1%
5.7212532
 
7.8%
5.7111336
 
7.1%
9.039894
 
6.2%
15.499219
 
5.7%
13.668570
 
5.3%
21.65450
 
3.4%
13.654924
 
3.1%
15.54259
 
2.7%
Other values (46)35761
22.3%
ValueCountFrequency (%)
5.674
 
< 0.1%
5.68195
 
0.1%
5.692319
 
1.4%
5.72467
 
1.5%
5.7111336
7.1%
5.7212532
7.8%
7.9557
 
< 0.1%
7.96371
 
0.2%
7.971300
 
0.8%
7.98873
 
0.5%
ValueCountFrequency (%)
21.6145423
28.3%
21.65450
 
3.4%
21.592659
 
1.7%
21.584028
 
2.5%
21.573880
 
2.4%
21.56906
 
0.6%
21.55104
 
0.1%
21.5432
 
< 0.1%
21.5326
 
< 0.1%
21.5224
 
< 0.1%

P30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6311
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.7299676
Minimum136.17
Maximum570.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:20.127080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum136.17
5-th percentile138.37
Q1175.71
median341.69
Q3553.29
95-th percentile556.26
Maximum570.81
Range434.64
Interquartile range (IQR)377.58

Descriptive statistics

Standard deviation174.133835
Coefficient of variation (CV)0.4840681919
Kurtosis-1.703357546
Mean359.7299676
Median Absolute Deviation (MAD)202.86
Skewness-0.03923754329
Sum57685937.88
Variance30322.5925
MonotonicityNot monotonic
2022-04-13T20:18:20.294395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.61258
 
0.2%
553.76257
 
0.2%
553.58255
 
0.2%
553.9252
 
0.2%
553.62247
 
0.2%
553.72247
 
0.2%
553.54244
 
0.2%
553.91243
 
0.2%
553.47239
 
0.1%
553.56239
 
0.1%
Other values (6301)157878
98.5%
ValueCountFrequency (%)
136.171
 
< 0.1%
136.311
 
< 0.1%
136.381
 
< 0.1%
136.431
 
< 0.1%
136.451
 
< 0.1%
136.51
 
< 0.1%
136.511
 
< 0.1%
136.541
 
< 0.1%
136.561
 
< 0.1%
136.573
< 0.1%
ValueCountFrequency (%)
570.811
< 0.1%
570.491
< 0.1%
570.231
< 0.1%
570.221
< 0.1%
5701
< 0.1%
569.771
< 0.1%
569.742
< 0.1%
569.731
< 0.1%
569.71
< 0.1%
569.692
< 0.1%

Nf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1163
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2273.829707
Minimum1914.72
Maximum2388.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:20.469553image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1914.72
5-th percentile1915.36
Q12212.12
median2319.37
Q32388.05
95-th percentile2388.19
Maximum2388.64
Range473.92
Interquartile range (IQR)175.93

Descriptive statistics

Standard deviation142.4266133
Coefficient of variation (CV)0.0626373263
Kurtosis1.459525482
Mean2273.829707
Median Absolute Deviation (MAD)68.77
Skewness-1.513315638
Sum364629058
Variance20285.34018
MonotonicityNot monotonic
2022-04-13T20:18:20.652302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.082949
 
1.8%
2388.092944
 
1.8%
2388.072935
 
1.8%
2388.12857
 
1.8%
2388.062841
 
1.8%
2388.052822
 
1.8%
2388.112788
 
1.7%
2388.042688
 
1.7%
2388.122686
 
1.7%
2388.132535
 
1.6%
Other values (1153)132314
82.5%
ValueCountFrequency (%)
1914.721
 
< 0.1%
1914.771
 
< 0.1%
1914.781
 
< 0.1%
1914.792
< 0.1%
1914.82
< 0.1%
1914.821
 
< 0.1%
1914.834
< 0.1%
1914.841
 
< 0.1%
1914.853
< 0.1%
1914.862
< 0.1%
ValueCountFrequency (%)
2388.641
 
< 0.1%
2388.65
 
< 0.1%
2388.599
 
< 0.1%
2388.586
 
< 0.1%
2388.575
 
< 0.1%
2388.5621
< 0.1%
2388.5525
< 0.1%
2388.5428
< 0.1%
2388.5344
< 0.1%
2388.5243
< 0.1%

Nc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34161
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8677.553696
Minimum7984.51
Maximum9244.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:20.830921image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7984.51
5-th percentile8011.48
Q18334.77
median8764.2
Q39055.85
95-th percentile9081.91
Maximum9244.59
Range1260.08
Interquartile range (IQR)721.08

Descriptive statistics

Standard deviation374.6574536
Coefficient of variation (CV)0.04317546935
Kurtosis-1.300061206
Mean8677.553696
Median Absolute Deviation (MAD)304.12
Skewness-0.3722038082
Sum1391523833
Variance140368.2076
MonotonicityNot monotonic
2022-04-13T20:18:21.018161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9063.2237
 
< 0.1%
9058.8835
 
< 0.1%
9057.5534
 
< 0.1%
9055.7933
 
< 0.1%
9052.9833
 
< 0.1%
9056.3833
 
< 0.1%
9064.4833
 
< 0.1%
9064.7932
 
< 0.1%
9057.0232
 
< 0.1%
9060.9832
 
< 0.1%
Other values (34151)160025
99.8%
ValueCountFrequency (%)
7984.511
< 0.1%
7984.591
< 0.1%
7985.181
< 0.1%
7985.561
< 0.1%
7986.721
< 0.1%
7987.181
< 0.1%
7987.931
< 0.1%
7988.071
< 0.1%
7988.081
< 0.1%
7988.351
< 0.1%
ValueCountFrequency (%)
9244.591
< 0.1%
9239.761
< 0.1%
9234.351
< 0.1%
9228.911
< 0.1%
9228.531
< 0.1%
9228.331
< 0.1%
9226.61
< 0.1%
9224.871
< 0.1%
9224.531
< 0.1%
9223.561
< 0.1%

epr
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.153704812
Minimum0.93
Maximum1.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:21.181147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.93
5-th percentile0.94
Q11.02
median1.09
Q31.3
95-th percentile1.3
Maximum1.32
Range0.39
Interquartile range (IQR)0.28

Descriptive statistics

Standard deviation0.1421029466
Coefficient of variation (CV)0.123170975
Kurtosis-1.744087099
Mean1.153704812
Median Absolute Deviation (MAD)0.16
Skewness-0.1343680262
Sum185006.95
Variance0.02019324743
MonotonicityNot monotonic
2022-04-13T20:18:21.316211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.358624
36.6%
1.0238930
24.3%
0.9416770
 
10.5%
1.2616383
 
10.2%
1.0812257
 
7.6%
1.034651
 
2.9%
1.074325
 
2.7%
1.313778
 
2.4%
1.041227
 
0.8%
1.01794
 
0.5%
Other values (11)2620
 
1.6%
ValueCountFrequency (%)
0.93347
 
0.2%
0.9416770
10.5%
0.9524
 
< 0.1%
1.01794
 
0.5%
1.0238930
24.3%
1.034651
 
2.9%
1.041227
 
0.8%
1.05448
 
0.3%
1.062
 
< 0.1%
1.074325
 
2.7%
ValueCountFrequency (%)
1.32215
 
0.1%
1.313778
 
2.4%
1.358624
36.6%
1.2916
 
< 0.1%
1.285
 
< 0.1%
1.27769
 
0.5%
1.2616383
 
10.2%
1.25163
 
0.1%
1.169
 
< 0.1%
1.09562
 
0.4%

Ps30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct771
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.2120494
Minimum36.04
Maximum48.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:21.480791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum36.04
5-th percentile36.71
Q142.01
median44.93
Q347.34
95-th percentile47.83
Maximum48.53
Range12.49
Interquartile range (IQR)5.33

Descriptive statistics

Standard deviation3.426342183
Coefficient of variation (CV)0.07749792713
Kurtosis-0.1772750161
Mean44.2120494
Median Absolute Deviation (MAD)2.61
Skewness-0.8376560865
Sum7089800.03
Variance11.73982075
MonotonicityNot monotonic
2022-04-13T20:18:21.799943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.34902
 
0.6%
47.45878
 
0.5%
47.38878
 
0.5%
47.3864
 
0.5%
47.48853
 
0.5%
47.36847
 
0.5%
47.43847
 
0.5%
47.29843
 
0.5%
47.37843
 
0.5%
47.28842
 
0.5%
Other values (761)151762
94.6%
ValueCountFrequency (%)
36.041
 
< 0.1%
36.071
 
< 0.1%
36.081
 
< 0.1%
36.12
 
< 0.1%
36.112
 
< 0.1%
36.143
 
< 0.1%
36.156
< 0.1%
36.165
< 0.1%
36.177
< 0.1%
36.189
< 0.1%
ValueCountFrequency (%)
48.531
 
< 0.1%
48.521
 
< 0.1%
48.511
 
< 0.1%
48.481
 
< 0.1%
48.442
 
< 0.1%
48.433
< 0.1%
48.416
< 0.1%
48.46
< 0.1%
48.394
< 0.1%
48.384
< 0.1%

phi
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5982
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.7898206
Minimum128.31
Maximum537.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:21.986315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum128.31
5-th percentile130.35
Q1164.79
median321.69
Q3521.34
95-th percentile524.05
Maximum537.49
Range409.18
Interquartile range (IQR)356.55

Descriptive statistics

Standard deviation164.1934804
Coefficient of variation (CV)0.4846470303
Kurtosis-1.703998573
Mean338.7898206
Median Absolute Deviation (MAD)190.99
Skewness-0.03864897321
Sum54327996.84
Variance26959.49902
MonotonicityNot monotonic
2022-04-13T20:18:22.147859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521.69302
 
0.2%
130.56301
 
0.2%
130.57299
 
0.2%
521.46295
 
0.2%
130.65294
 
0.2%
130.52288
 
0.2%
521.42288
 
0.2%
521.66283
 
0.2%
130.54283
 
0.2%
130.59283
 
0.2%
Other values (5972)157443
98.2%
ValueCountFrequency (%)
128.311
 
< 0.1%
128.451
 
< 0.1%
128.461
 
< 0.1%
128.542
< 0.1%
128.561
 
< 0.1%
128.571
 
< 0.1%
128.581
 
< 0.1%
128.592
< 0.1%
128.623
< 0.1%
128.711
 
< 0.1%
ValueCountFrequency (%)
537.491
 
< 0.1%
537.41
 
< 0.1%
537.351
 
< 0.1%
537.31
 
< 0.1%
537.111
 
< 0.1%
537.061
 
< 0.1%
536.823
< 0.1%
536.812
< 0.1%
536.81
 
< 0.1%
536.781
 
< 0.1%

NRf
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct567
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2349.645243
Minimum2027.57
Maximum2390.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:22.318504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2027.57
5-th percentile2028.25
Q12387.97
median2388.07
Q32388.16
95-th percentile2388.4
Maximum2390.49
Range362.92
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation111.1672423
Coefficient of variation (CV)0.04731235178
Kurtosis4.475126672
Mean2349.645243
Median Absolute Deviation (MAD)0.09
Skewness-2.544608793
Sum376786761.5
Variance12358.15576
MonotonicityNot monotonic
2022-04-13T20:18:22.507019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2388.095257
 
3.3%
2388.15232
 
3.3%
2388.075205
 
3.2%
2388.085143
 
3.2%
2388.115031
 
3.1%
2388.065028
 
3.1%
2388.054934
 
3.1%
2388.124913
 
3.1%
2388.044656
 
2.9%
2388.134483
 
2.8%
Other values (557)110477
68.9%
ValueCountFrequency (%)
2027.571
 
< 0.1%
2027.61
 
< 0.1%
2027.612
< 0.1%
2027.641
 
< 0.1%
2027.651
 
< 0.1%
2027.661
 
< 0.1%
2027.672
< 0.1%
2027.694
< 0.1%
2027.72
< 0.1%
2027.711
 
< 0.1%
ValueCountFrequency (%)
2390.491
< 0.1%
2390.481
< 0.1%
2390.391
< 0.1%
2390.381
< 0.1%
2390.321
< 0.1%
2390.281
< 0.1%
2390.271
< 0.1%
2390.241
< 0.1%
2390.231
< 0.1%
2390.211
< 0.1%

NRc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20090
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8088.950972
Minimum7845.78
Maximum8293.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:22.698997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum7845.78
5-th percentile7875.22
Q18070.53
median8118.59
Q38139.41
95-th percentile8162.051
Maximum8293.72
Range447.94
Interquartile range (IQR)68.88

Descriptive statistics

Standard deviation80.62325701
Coefficient of variation (CV)0.009967084395
Kurtosis2.28395995
Mean8088.950972
Median Absolute Deviation (MAD)31.04
Skewness-1.733426856
Sum1297136089
Variance6500.109571
MonotonicityNot monotonic
2022-04-13T20:18:22.869980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8140.0149
 
< 0.1%
8138.8948
 
< 0.1%
8138.7646
 
< 0.1%
8140.645
 
< 0.1%
8143.6144
 
< 0.1%
814143
 
< 0.1%
8138.3143
 
< 0.1%
8135.8542
 
< 0.1%
8136.5742
 
< 0.1%
8142.4442
 
< 0.1%
Other values (20080)159915
99.7%
ValueCountFrequency (%)
7845.781
< 0.1%
7848.361
< 0.1%
7848.431
< 0.1%
7849.261
< 0.1%
7849.791
< 0.1%
7850.231
< 0.1%
7850.961
< 0.1%
7851.032
< 0.1%
7851.31
< 0.1%
7851.81
< 0.1%
ValueCountFrequency (%)
8293.721
< 0.1%
8290.551
< 0.1%
8290.251
< 0.1%
8289.631
< 0.1%
8288.261
< 0.1%
8282.51
< 0.1%
8279.861
< 0.1%
8279.791
< 0.1%
8278.391
< 0.1%
8277.761
< 0.1%

BPR
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13124
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.054746852
Minimum8.1563
Maximum11.0669
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:23.043031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.1563
5-th percentile8.3584
Q18.43925
median9.0301
Q39.3442
95-th percentile10.8963
Maximum11.0669
Range2.9106
Interquartile range (IQR)0.90495

Descriptive statistics

Standard deviation0.7515810269
Coefficient of variation (CV)0.08300409047
Kurtosis1.038994429
Mean9.054746852
Median Absolute Deviation (MAD)0.4945
Skewness1.312525089
Sum1452010.15
Variance0.56487404
MonotonicityNot monotonic
2022-04-13T20:18:23.214362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.430375
 
< 0.1%
8.446875
 
< 0.1%
8.412874
 
< 0.1%
8.430972
 
< 0.1%
8.413672
 
< 0.1%
8.431871
 
< 0.1%
8.439171
 
< 0.1%
8.438271
 
< 0.1%
8.444670
 
< 0.1%
8.439270
 
< 0.1%
Other values (13114)159638
99.6%
ValueCountFrequency (%)
8.15631
< 0.1%
8.17571
< 0.1%
8.17871
< 0.1%
8.17931
< 0.1%
8.1851
< 0.1%
8.18891
< 0.1%
8.19321
< 0.1%
8.19511
< 0.1%
8.19611
< 0.1%
8.19651
< 0.1%
ValueCountFrequency (%)
11.06691
< 0.1%
11.06631
< 0.1%
11.06571
< 0.1%
11.06451
< 0.1%
11.06421
< 0.1%
11.05721
< 0.1%
11.05471
< 0.1%
11.05451
< 0.1%
11.05372
< 0.1%
11.05341
< 0.1%

farB
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.03
83152 
0.02
77207 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.02
2nd row0.02
3rd row0.02
4th row0.02
5th row0.02

Common Values

ValueCountFrequency (%)
0.0383152
51.9%
0.0277207
48.1%

Length

2022-04-13T20:18:23.374320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T20:18:23.462986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0383152
51.9%
0.0277207
48.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

htBleed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct58
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean360.6988008
Minimum302
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:23.571916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum302
5-th percentile307
Q1332
median367
Q3392
95-th percentile395
Maximum400
Range98
Interquartile range (IQR)60

Descriptive statistics

Standard deviation31.02143012
Coefficient of variation (CV)0.08600369629
Kurtosis-1.362355909
Mean360.6988008
Median Absolute Deviation (MAD)27
Skewness-0.342815659
Sum57841299
Variance962.3291266
MonotonicityNot monotonic
2022-04-13T20:18:23.749652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39314527
 
9.1%
39214431
 
9.0%
39410281
 
6.4%
3918820
 
5.5%
3317931
 
4.9%
3307103
 
4.4%
3326879
 
4.3%
3336495
 
4.1%
3955973
 
3.7%
3345580
 
3.5%
Other values (48)72339
45.1%
ValueCountFrequency (%)
3023
 
< 0.1%
30338
 
< 0.1%
304378
 
0.2%
3051694
 
1.1%
3063868
2.4%
3074877
3.0%
3083697
2.3%
3091849
 
1.2%
310608
 
0.4%
311122
 
0.1%
ValueCountFrequency (%)
4001
 
< 0.1%
39920
 
< 0.1%
398209
 
0.1%
3971158
 
0.7%
3963117
 
1.9%
3955973
3.7%
39410281
6.4%
39314527
9.1%
39214431
9.0%
3918820
5.5%

Nf_dmd
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2273.754039
Minimum1915
Maximum2388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:23.889133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1915
5-th percentile1915
Q12212
median2319
Q32388
95-th percentile2388
Maximum2388
Range473
Interquartile range (IQR)176

Descriptive statistics

Standard deviation142.5131144
Coefficient of variation (CV)0.06267745407
Kurtosis1.464774864
Mean2273.754039
Median Absolute Deviation (MAD)69
Skewness-1.515307281
Sum364616924
Variance20309.98777
MonotonicityNot monotonic
2022-04-13T20:18:24.004481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
238862633
39.1%
221228853
18.0%
231917320
 
10.8%
232417213
 
10.7%
222317199
 
10.7%
191517141
 
10.7%
ValueCountFrequency (%)
191517141
 
10.7%
221228853
18.0%
222317199
 
10.7%
231917320
 
10.8%
232417213
 
10.7%
238862633
39.1%
ValueCountFrequency (%)
238862633
39.1%
232417213
 
10.7%
231917320
 
10.8%
222317199
 
10.7%
221228853
18.0%
191517141
 
10.7%

PCNfR_dmd
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
100.0
143218 
84.93
17141 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100.0
2nd row100.0
3rd row100.0
4th row100.0
5th row84.93

Common Values

ValueCountFrequency (%)
100.0143218
89.3%
84.9317141
 
10.7%

Length

2022-04-13T20:18:24.133053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-13T20:18:24.225338image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
100.0143218
89.3%
84.9317141
 
10.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

W31
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct676
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.94270855
Minimum10.16
Maximum39.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:24.332578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum10.16
5-th percentile10.54
Q114.33
median24.92
Q338.82
95-th percentile39.16
Maximum39.89
Range29.73
Interquartile range (IQR)24.49

Descriptive statistics

Standard deviation11.69142189
Coefficient of variation (CV)0.4506631168
Kurtosis-1.682443318
Mean25.94270855
Median Absolute Deviation (MAD)13.78
Skewness-0.06933399254
Sum4160146.8
Variance136.6893459
MonotonicityNot monotonic
2022-04-13T20:18:24.634447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.921187
 
0.7%
38.861176
 
0.7%
38.891163
 
0.7%
38.951157
 
0.7%
38.911146
 
0.7%
38.881146
 
0.7%
38.871144
 
0.7%
38.91139
 
0.7%
38.831135
 
0.7%
38.971132
 
0.7%
Other values (666)148834
92.8%
ValueCountFrequency (%)
10.161
 
< 0.1%
10.182
 
< 0.1%
10.192
 
< 0.1%
10.21
 
< 0.1%
10.212
 
< 0.1%
10.223
< 0.1%
10.236
< 0.1%
10.244
< 0.1%
10.251
 
< 0.1%
10.267
< 0.1%
ValueCountFrequency (%)
39.892
 
< 0.1%
39.851
 
< 0.1%
39.842
 
< 0.1%
39.824
 
< 0.1%
39.812
 
< 0.1%
39.82
 
< 0.1%
39.795
< 0.1%
39.788
< 0.1%
39.772
 
< 0.1%
39.7611
< 0.1%

W32
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26659
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.56570042
Minimum6.0105
Maximum23.9505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:24.810979image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum6.0105
5-th percentile6.3217
Q18.6013
median14.9535
Q323.2946
95-th percentile23.4957
Maximum23.9505
Range17.94
Interquartile range (IQR)14.6933

Descriptive statistics

Standard deviation7.015066959
Coefficient of variation (CV)0.4506746736
Kurtosis-1.682372314
Mean15.56570042
Median Absolute Deviation (MAD)8.268
Skewness-0.06936640449
Sum2496100.154
Variance49.21116443
MonotonicityNot monotonic
2022-04-13T20:18:24.976581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.366435
 
< 0.1%
23.300634
 
< 0.1%
23.322233
 
< 0.1%
23.410933
 
< 0.1%
23.315432
 
< 0.1%
23.392332
 
< 0.1%
23.330431
 
< 0.1%
23.343431
 
< 0.1%
23.27231
 
< 0.1%
23.324731
 
< 0.1%
Other values (26649)160036
99.8%
ValueCountFrequency (%)
6.01051
< 0.1%
6.08041
< 0.1%
6.08431
< 0.1%
6.09181
< 0.1%
6.10081
< 0.1%
6.11851
< 0.1%
6.1291
< 0.1%
6.13761
< 0.1%
6.14081
< 0.1%
6.14271
< 0.1%
ValueCountFrequency (%)
23.95051
< 0.1%
23.91591
< 0.1%
23.91541
< 0.1%
23.90751
< 0.1%
23.90221
< 0.1%
23.89741
< 0.1%
23.89711
< 0.1%
23.8941
< 0.1%
23.88812
< 0.1%
23.88791
< 0.1%

failure
Real number (ℝ≥0)

HIGH CORRELATION

Distinct214
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean245.6626756
Minimum128
Maximum543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:25.156985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum128
5-th percentile154
Q1191
median226
Q3285
95-th percentile392
Maximum543
Range415
Interquartile range (IQR)94

Descriptive statistics

Standard deviation76.47537933
Coefficient of variation (CV)0.3113023952
Kurtosis1.506209068
Mean245.6626756
Median Absolute Deviation (MAD)43
Skewness1.204509773
Sum39394221
Variance5848.483644
MonotonicityNot monotonic
2022-04-13T20:18:25.323858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1942522
 
1.6%
2022424
 
1.5%
1842392
 
1.5%
1932123
 
1.3%
2972079
 
1.3%
1702040
 
1.3%
2051845
 
1.2%
2001800
 
1.1%
1991791
 
1.1%
3441720
 
1.1%
Other values (204)139623
87.1%
ValueCountFrequency (%)
128384
0.2%
129129
 
0.1%
131131
 
0.1%
133133
 
0.1%
134134
 
0.1%
135270
0.2%
136136
 
0.1%
137274
0.2%
139139
 
0.1%
140140
 
0.1%
ValueCountFrequency (%)
543543
0.3%
525525
0.3%
494494
0.3%
491491
0.3%
489489
0.3%
481481
0.3%
459459
0.3%
457457
0.3%
447447
0.3%
446446
0.3%

RUL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct543
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.3313378
Minimum0
Maximum542
Zeros709
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2022-04-13T20:18:25.496816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q156
median113
Q3172
95-th percentile275
Maximum542
Range542
Interquartile range (IQR)116

Descriptive statistics

Standard deviation83.53814553
Coefficient of variation (CV)0.6828842636
Kurtosis0.8325328473
Mean122.3313378
Median Absolute Deviation (MAD)58
Skewness0.8471574213
Sum19616931
Variance6978.621759
MonotonicityNot monotonic
2022-04-13T20:18:25.681146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49709
 
0.4%
81709
 
0.4%
94709
 
0.4%
93709
 
0.4%
92709
 
0.4%
91709
 
0.4%
90709
 
0.4%
89709
 
0.4%
88709
 
0.4%
87709
 
0.4%
Other values (533)153269
95.6%
ValueCountFrequency (%)
0709
0.4%
1709
0.4%
2709
0.4%
3709
0.4%
4709
0.4%
5709
0.4%
6709
0.4%
7709
0.4%
8709
0.4%
9709
0.4%
ValueCountFrequency (%)
5421
< 0.1%
5411
< 0.1%
5401
< 0.1%
5391
< 0.1%
5381
< 0.1%
5371
< 0.1%
5361
< 0.1%
5351
< 0.1%
5341
< 0.1%
5331
< 0.1%

Interactions

2022-04-13T20:18:08.196989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:15:51.900543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:15:57.384666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:02.692503image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:07.868864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:13.046578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:18.152216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:23.275993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:28.608121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:33.801326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:39.301498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:44.387809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:49.522131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:55.018779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:00.076818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:05.249527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:10.524495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:15.891378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:21.155027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:26.282680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:31.699044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:36.920475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:42.173360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:47.442337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:52.646957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:57.909431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:18:03.182446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:18:08.389551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:15:52.137395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:15:57.590129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:02.890380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:08.067624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:13.247430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:18.348849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:23.479807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:28.805612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:34.008961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:39.500151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:44.586253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:49.733404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:00.276306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:05.453933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:10.731161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:16.090597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:21.349672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:26.496585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:31.892865image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:37.120661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:52.851281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:58.115158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:18:03.374451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:15:52.334950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:16:39.686408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:05.643302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:10.924078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:16.283330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:21.543071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:26.695335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:32.096558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:37.315437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:42.575586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:47.831468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:53.051389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:58.307733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:18:03.561504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:15:52.528124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:16:23.865495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:29.181340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:16:40.011804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:11.117094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:16.479713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:21.721222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:26.889411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:32.278852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:42.762263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:48.015427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:53.243091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:58.494847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:18:03.739695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:15:52.721158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:15:58.159930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:03.448146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:08.625756image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:13.795405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:16:18.903200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:16:29.360808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:16.662742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:17:27.083762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:32.463424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:37.695925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:42.949224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-04-13T20:17:48.201500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-04-13T20:18:08.019355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-04-13T20:18:25.886009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-13T20:18:26.238276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-13T20:18:26.585811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-13T20:18:26.873180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-13T20:18:27.059080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-13T20:18:13.491082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-13T20:18:14.632683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0Unnamed: 0.1unit_IDcyclessetting_1setting_2setting_3T2T24T30T50P2P15P30NfNceprPs30phiNRfNRcBPRfarBhtBleedNf_dmdPCNfR_dmdW31W32failureRUL
0001142.00490.8400100.0445.00549.681343.431112.933.915.70137.362211.868311.321.0141.69129.782387.998074.839.33350.023302212100.0010.626.3670321320
1111220.00200.7002100.0491.19606.071477.611237.509.3513.61332.102323.668713.601.0743.94312.592387.738046.139.19130.023612324100.0024.3714.6552321319
2221342.00380.8409100.0445.00548.951343.121117.053.915.69138.182211.928306.691.0141.66129.622387.978066.629.40070.023292212100.0010.486.4213321318
3331442.00000.8400100.0445.00548.701341.241118.033.915.70137.982211.888312.351.0241.68129.802388.028076.059.33690.023282212100.0010.546.4176321317
4441525.00630.620760.0462.54536.101255.231033.597.059.00174.821915.227994.940.9336.48164.112028.087865.8010.83660.02305191584.9314.038.6754321316
5551634.99960.8400100.0449.44554.771352.871117.015.487.97193.822222.778340.001.0241.44181.902387.878054.109.33460.023302223100.0014.918.9057321315
666170.00190.0001100.0518.67641.831583.471393.8914.6221.58552.452387.929050.501.3046.94520.482387.898127.928.39600.033912388100.0038.9323.4578321314
7771841.99810.8400100.0445.00549.051344.161110.773.915.69137.132211.928307.281.0141.60129.652387.978075.999.36790.023292212100.0010.556.2787321313
8881942.00160.8400100.0445.00549.551342.851101.673.915.70138.022211.908307.811.0241.44129.652388.008071.139.33840.023282212100.0010.636.3055321312
99911025.00190.621760.0462.54536.351251.911041.377.059.01174.701915.238005.830.9436.24164.082028.137869.4110.91410.02305191584.9314.348.6119321311

Last rows

Unnamed: 0Unnamed: 0.1unit_IDcyclessetting_1setting_2setting_3T2T24T30T50P2P15P30NfNceprPs30phiNRfNRcBPRfarBhtBleedNf_dmdPCNfR_dmdW31W32failureRUL
160349160349537492603070.00010.0000100.0518.67643.231598.431428.3314.6221.61551.752388.129186.001.3048.12519.872388.098237.528.52460.033952388100.0038.8223.11353169
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